发表机构
Xiamen University; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Tohoku University（厦门大学; 中国科学院深圳先进技术研究院; 东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对分子动力学预测固态电解质离子电导率的可靠性问题，提出以局部均方位移指数α(t)作为定量判据，评估动力学收敛并确定最小轨迹长度，从而提升材料排序准确性和资源利用效率。
AI 中文摘要
分子动力学被广泛用于预测固态电解质中的离子电导率，但这些预测的可靠性往往难以评估。我们的分析确定有限轨迹长度、有限晶胞尺寸和采样不足是有限原子离子输运计算的内在局限性。以立方相Li7La3Zr2O12作为代表性固态电解质来量化这些局限性的后果。这些局限性可能隐藏在看似线性的均方位移和表现良好的Arrhenius关系背后，导致扩散系数、活化能和外推离子电导率不准确。此类不准确可能错误地对候选固态电解质进行排序，从而误导计算筛选和实验验证。在此，我们建立局部均方位移指数α(t)作为定量可靠性判据，将动力学收敛与扩散系数和Nernst-Einstein离子电导率的误差联系起来。该判据还进一步确定在给定温度下达到规定精度所需的最小轨迹长度。独立重复样本可减少统计不确定性，而选择性单轴扩展可减轻有限尺寸效应。通过确定模拟离子电导率何时在定量上可信，该方法能够实现更可靠的材料排序和更高效地利用计算与实验资源，从而加速开发用于固态电池的高性能固态电解质。
英文摘要
Molecular dynamics is widely used to predict ionic conductivity in solid electrolytes, but the reliability of these predictions is often difficult to assess. Our analysis identifies finite trajectory length, limited cell size, and insufficient sampling as intrinsic limitations of finite atomistic ion-transport calculations. Cubic Li7La3Zr2O12 is used as a representative solid electrolyte to quantify their consequences. These limitations can remain hidden behind apparently linear mean-squared displacements and well-behaved Arrhenius relations, leading to inaccurate diffusivities, activation energies, and extrapolated ionic conductivities. Such inaccuracies can misrank candidate solid electrolytes and consequently misdirect computational screening and experimental validation. Here, we establish the local mean-squared-displacement exponent, α(t), as a quantitative reliability criterion that links dynamical convergence to errors in diffusivity and Nernst-Einstein ionic conductivity. The criterion further determines the minimum trajectory length required to achieve a prescribed accuracy as a function of temperature. Independent replicas reduce statistical uncertainty, while selective single-axis expansion mitigates finite-size effects. By establishing when simulated ionic conductivity is quantitatively trustworthy, this approach enables more reliable materials ranking and more efficient use of computational and experimental resources, thereby accelerating the development of high-performance solid electrolytes for solid-state batteries.
Comments14 pages, 5 figures
